Instructions to use hf-tiny-model-private/tiny-random-WavLMModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-WavLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-WavLMModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-WavLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-WavLMModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c296efa1c206a49c4637c994b8aa5a0bb62b721315c0787397e619b49b78f56
- Size of remote file:
- 120 kB
- SHA256:
- eefa9a73ad0f75c6b394895ba1073ec39fed1b3067c3fee6b4184c1f2f8ae951
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